A novel cylindrical filtering-based greedy perimeter stateless routing scheme in flying ad hoc networks

Abstract

Flying ad hoc networks (FANETs) are a new example of ad hoc networks, which arrange unmanned aerial vehicles (UAVs) in an ad hoc form. The features of these networks, such as the movement of UAVs in a 3D space, high speed of UAVs, dynamic topology, limited resources, and low density, have created vital challenges for communication reliability, especially when designing routing methods in FANETs. In this paper, a novel cylindrical filtering-based greedy perimeter stateless routing scheme (CF-GPSR) is suggested in FANETs. In CF-GPSR, cylindrical filtering reduces the size of the initial candidate set to accelerate the selection of the next-hop node. In this phase, the formulation of the cylindrical filtering construction process is expressed in the cylindrical coordinate system because the filtered area is a cylinder enclosed within the communication range of flying nodes. The cylindrical filtering construction process includes three steps, namely transferring coordinate axes, rotating coordinate axes, and cylinder construction. When selecting the next-hop node, CF-GPSR first uses this cylindrical filtering to limit the candidate set of each flying node. Then, CF-GPSR decides on the best next-hop UAV based on a merit function, which includes four criteria, namely velocity factor, ideal distance, residual energy, and movement angle, and selects a candidate node with the highest merit value as the next-hop UAV. Finally, the simulation process is performed using the NS 3.23 simulator, and four simulation scenarios are defined based on the number of UAVs, the communication area of nodes, network connections, and the size of packets to evaluate CF-GPSR. In the simulation process, CF-GPSR is compared with the three GPSR-based routing schemes, namely UF-GPSR, GPSR-PPU, and GPSR in terms of delay, data delivery ratio, data loss ratio, and throughput. In the first scenario, namely the change in the number of flying nodes, CF-GPSR improves delay, PDR, PLR, and throughput by 17.34%, 4.83%, 16%, and 7.05%, respectively. Also, in the second scenario, namely the change in communication range, the proposed method optimizes delay, PDR, PLR, and throughput by 4.91%, 5.71%, 6.12%, and 8.45%, respectively. In the third scenario, namely the change in the number of connections, CF-GPSR improves EED, PDR, PLR, and throughput by 18.41%, 9.09%, 9.52%, and 7.03%, respectively. In the fourth simulation scenario, namely the change in the packet size, CF-GPSR improves delay, PDR, PLR, and throughput by 14.81%, 19.39%, 7.19%, and 0.39%, respectively.